Dynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems - from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial - with clearly spelled-out and unified nomenclature - derived from the author's own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications. * Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics; PLUS ...* The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of "math modeling with life sciences.* Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization.* Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models.* A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course. Importantly, the slides are editable, so they can be readily adapted to a lecturer's personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content. The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: joed@cs.ucla.edu
Show moreDynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems - from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial - with clearly spelled-out and unified nomenclature - derived from the author's own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications. * Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics; PLUS ...* The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of "math modeling with life sciences.* Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization.* Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models.* A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course. Importantly, the slides are editable, so they can be readily adapted to a lecturer's personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content. The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: joed@cs.ucla.edu
Show moreA comprehensive textbook and reference on contemporary dynamical biosystem modeling and simulation methodology
1. Biosystem Modeling and Simulation: Nomenclature and Philosophy2. Math Models of Systems: Biomodeling 1013. Computer Simulation Methods4. Structural Biomodeling from Theory & Data: Compartmentalizations5. Structural Biomodeling from Theory & Data: Sizing, Distinguishing & Simplifying Multicompartmental Models6. Nonlinear Mass Action & Biochemical Kinetic Interaction Modeling7. Cellular Systems Biology Modeling: Deterministic & Stochastic8. Physiologically Based, Whole-Organism Kinetics & Noncompartmental Modeling9. Biosystem Stability & Oscillations10. Structural Identifiability11. Parameter Sensitivity Methods12. Parameter Estimation & Numerical Identifiability13. Parameter Estimation Methods II: Facilitating, Simplifying & Working With Data14. Biocontrol System Modeling, Simulation, and Analysis15. Data-Driven Modeling and Alternative Hypothesis Testing16. Experiment Design and Optimization17. Model Reduction and Network Inference in Dynamic Systems Biology
“Professor Joe – as he is called by his students – is a
Distinguished Professor of Computer Science and Medicine and Chair
of the Computational & Systems Biology Interdepartmental Program at
UCLA – an undergraduate research-oriented program he nurtured and
honed over several decades. As an active full-time member of the
UCLA faculty for nearly half a century, he also developed and led
innovative graduate PhD programs, including Computational Systems
Biology in Computer Science, and Biosystem Science and Engineering
in Biomedical Engineering. He has mentored students from these
programs since 1968, as Director of the UCLA Biocybernetics
Laboratory, and was awarded the prestigious UCLA Distinguished
Teaching Award and Eby Award for Creative Teaching in 2003, and the
Lockeed-Martin Award for Teaching Excellence in 2004. Professor Joe
also is a Fellow of the Biomedical Engineering Society. Visiting
professorships included stints at universities in Canada, Italy,
Sweden and the UK and he was a Senior Fulbright-Hays Scholar in
Italy in 1979.
Professor Joe has been very active in the publishing world. As an
editor, he founded and was Editor-in-Chief of the Modeling
Methodology Forum – a department in seven of the American Journals
of Physiology – from 1984 thru 1991. As a writer, he authored or
coauthored both editions of Feedback and Control Systems
(Schaum-McGraw-Hill 1967 and 1990), more than 200 research
articles, and recently published his opus textbook: Dynamic Systems
Biology Modeling and Simulation (Academic Press/Elsevier November
2013 and February 2014).
Much of his research has been based on integrating experimental
neuroendocrine and metabolism studies in mammals and fishes with
data-driven mathematical modeling methodology – strongly motivated
by his experiences in “wet-lab. His seminal contributions to
modeling theory and practice are in structural identifiability
(parameter ambiguity) analysis, driven by experimental
encumbrances. He introduced the notions of interval and
quasi-identifiablity of unidentifiable dynamic system models, and
his lab has developed symbolic algorithmic approaches and new
internet software (web app COMBOS) for computing identifiable
parameter combinations. These are the aggregate parts of otherwise
unidentifiable models that can be quantified – with broad
application in model reduction (simplification) and experiment
design. His long-term contributions to quantitative understanding
of thyroid hormone production and metabolism in mammals and fishes
have recently been crystallized into web app THYROSIM – for
internet-based research and teaching about thyroid hormone dynamics
in humans.
Last but not least, Professor Joe is a passionate straight-ahead
jazz saxophone player (alto and tenor), an alternate career begun
in the 1950s in NYC at Stuyvesant High School – temporarily
suspended when he started undergrad school, and resumed again in
middle-age. He recently added flute to his practice schedule and he
and his band – Acoustically Speaking –can be found occasionally
gigging in Los Angeles or Honolulu haunts.
"I am just in awe of your ability to start with simple ideas and use them to explain sophisticated concepts and methodologies in modeling biochemical and cellular systems (Chapters 6 and 7). This is a great new contribution to the textbook offerings in systems biology." Alex Hoffmann, Director of the San Diego Center for Systems Biology and the UCSD Graduate Program in Bioinformatics and Systems Biology "I found Chapter 1 to be a marvel of heavy-lifting, done so smoothly there was no detectable sweat. Heavy-lifting because you laid out the big load of essential vocabulary and concepts a reader has to have to enter the world of biomodeling confidently. In that chapter you generously acknowledge some us who tried to accomplish this earlier but, compared to your Chapter 1, we were clumsy and boring. For me, now, Chapter 1 was a "page-turner" to be enjoyed straight through. You have the gift of a master athlete who does impossible performances and makes them seem easy. "Your Chapter 9 - on oscillations and stability - is a true jewel. I have a shelf full of books etc on nonlinear mechanics and system analyses and modeling, but nothing to match the clarity and deep understanding you offer the reader. You are a great explainer and teacher." F. Eugene Yates, Emeritus Professor of Medicine, Chemical Engineering and Ralph and Marjorie Crump Professor of Biomedical Engineering, UCLA "Chapter 4 covers many aspects of the notion of compartmentalization in the structural modeling of biomedical and biological models - both linear and nonlinear. Developments are biophysically motivated throughout; and compartments are taken to represent entities with the same dynamic characteristics (dynamic signatures). A very positive feature of this text is the numerous worked examples in the text, which greatly help readers follow the material. At the end of the chapter, there are further well thought out analytical and simulation exercises that will help readers check that they have understood what has been presented. "Chapter 5 looks at many important aspects of multicompartmental modeling, examining in more detail how output data limit what can be learnt about model structure, even when such data are perfect. Among the many features explained are how to establish the size and complexity of a model; how to select between several candidate models; and whether it is possible to simplify a model. All of this is done with respect to the dynamic signatures in the model. As in Chapter 4, readers are helped to understand the often challenging material by means of numerous worked examples in the text, and there are further examples given at the end." Professor Keith Godfrey, University of Warwick, Coventry, U.K.
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